Arvind Panwar – författare

Explainable AI in Clinical Practice: Methods, Applications, and Implementation bridges the gap between artificial intelligence capabilities and their practical implementation in healthcare. The book explores applications of explainable AI in diagnostic support and treatment planning, offering insights into making AI systems interpretable and accountable. Through real-world case studies and ethical frameworks, readers learn to transform opaque AI systems into tools that enhance clinical practice while maintaining high patient care standards. This volume unites leading experts to provide a comprehensive framework for implementing explainable AI, ensuring that AI-driven decisions are transparent, trustworthy, and clinically sound. Targeted solutions in the book cater to diverse stakeholders in the healthcare AI ecosystem. Healthcare professionals will gain confidence in integrating AI tools, while technical teams will receive implementation guidelines. This book is essential for anyone seeking to responsibly and effectively navigate the complexities of AI in healthcare.Provides a comprehensive framework for implementing explainable AI in healthcare, ensuring that AI-driven decisions are transparent, trustworthy, and clinically soundIncludes real-world case studies that illustrate practical applications of explainable AIOffers targeted solutions for diverse stakeholders in the healthcare AI ecosystem

Explainable AI in Clinical Practice: Methods, Applications, and Implementation bridges the gap between artificial intelligence capabilities and their practical implementation in healthcare. The book explores applications of explainable AI in diagnostic support and treatment planning, offering insights into making AI systems interpretable and accountable. Through real-world case studies and ethical frameworks, readers learn to transform opaque AI systems into tools that enhance clinical practice while maintaining high patient care standards. This volume unites leading experts to provide a comprehensive framework for implementing explainable AI, ensuring that AI-driven decisions are transparent, trustworthy, and clinically sound. Targeted solutions in the book cater to diverse stakeholders in the healthcare AI ecosystem. Healthcare professionals will gain confidence in integrating AI tools, while technical teams will receive implementation guidelines. This book is essential for anyone seeking to responsibly and effectively navigate the complexities of AI in healthcare. - Provides a comprehensive framework for implementing explainable AI in healthcare, ensuring that AI-driven decisions are transparent, trustworthy, and clinically sound- Includes real-world case studies that illustrate practical applications of explainable AI- Offers targeted solutions for diverse stakeholders in the healthcare AI ecosystem

Explainable AI in Clinical Practice: Advanced Applications and Future Directions builds on foundational concepts to explore the practical implementation and emerging trends of transparent AI in healthcare. Featuring contributions from leading experts, this volume presents advanced methodologies, real-world case studies across various medical specialties, and strategies for overcoming ethical, regulatory, and operational challenges. The book offers comprehensive frameworks for integrating explainable AI into clinical workflows, emphasizing trust, patient understanding, and regulatory compliance. In addition, it examines future technologies such as federated learning, multimodal systems, and human-AI collaboration, providing insights into the evolving landscape of AI in medicine.Essential for healthcare professionals, researchers, and policymakers, this volume aims to accelerate the responsible adoption of explainable AI, ultimately enhancing patient care, clinical decision-making, and healthcare system efficiency.Provides comprehensive implementation frameworks that guide the deployment of explainable AI in healthcare, addressing technical, organizational, ethical, and regulatory challengesPresents detailed, specialty-specific case studies that demonstrate successful real-world applications of explainable AI across various clinical disciplinesExplores future directions and emerging technologies, offering insights into how explainable AI will integrate with innovations like federated learning and multimodal systems to shape healthcare’s evolution

Explainable AI in Clinical Practice: Advanced Applications and Future Directions builds on foundational concepts to explore the practical implementation and emerging trends of transparent AI in healthcare. Featuring contributions from leading experts, this volume presents advanced methodologies, real-world case studies across various medical specialties, and strategies for overcoming ethical, regulatory, and operational challenges. The book offers comprehensive frameworks for integrating explainable AI into clinical workflows, emphasizing trust, patient understanding, and regulatory compliance. In addition, it examines future technologies such as federated learning, multimodal systems, and human-AI collaboration, providing insights into the evolving landscape of AI in medicine.Essential for healthcare professionals, researchers, and policymakers, this volume aims to accelerate the responsible adoption of explainable AI, ultimately enhancing patient care, clinical decision-making, and healthcare system efficiency. - Provides comprehensive implementation frameworks that guide the deployment of explainable AI in healthcare, addressing technical, organizational, ethical, and regulatory challenges- Presents detailed, specialty-specific case studies that demonstrate successful real-world applications of explainable AI across various clinical disciplines- Explores future directions and emerging technologies, offering insights into how explainable AI will integrate with innovations like federated learning and multimodal systems to shape healthcare's evolution

Tackle the critical challenge of sustainability in modern technology with this essential book, which provides a comprehensive, expert-led exploration of energy-aware methodologies and machine learning strategies for optimizing IoT-based wireless sensor networks. With the rapid expansion of IoT applications in diverse domains such as smart cities, agriculture, healthcare, and industry, energy constraints pose significant challenges to maintaining sustainable operations. This book addresses these challenges by presenting a comprehensive exploration of methodologies, technologies, and strategies aimed at optimizing energy usage in IoT-based wireless sensor networks. Authored by experts from academia and industry, the book covers topics such as energy-aware routing protocols, edge computing, energy harvesting technologies, machine learning applications, and blockchain-based energy management frameworks. Each chapter provides cutting-edge insights and practical approaches to fostering energy efficiency while ensuring robust and scalable IoT solutions. This book serves as a valuable resource for researchers, professionals, and policymakers, offering actionable knowledge to navigate the evolving landscape of IoT and wireless sensor network technologies. Readers will find the volume: Explores the intersection of Internet of Things, wireless sensor networks, and energy efficiency across fundamental concepts, protocols, applications, and advanced solutions;Features chapters by leading researchers and industry professionals, providing authoritative perspectives;Offers actionable insights for implementing sustainable and energy-efficient IoT solutions in diverse fields like healthcare, agriculture, and smart cities;Highlights emerging trends, including AI, machine learning, and blockchain integration in wireless sensor networks.Audience Researchers, academics, engineers, system architects, technology developers, and policymakers specializing in IoT, wireless communications, and energy-efficient systems.

Tackle the critical challenge of sustainability in modern technology with this essential book, which provides a comprehensive, expert-led exploration of energy-aware methodologies and machine learning strategies for optimizing IoT-based wireless sensor networks. With the rapid expansion of IoT applications in diverse domains such as smart cities, agriculture, healthcare, and industry, energy constraints pose significant challenges to maintaining sustainable operations. This book addresses these challenges by presenting a comprehensive exploration of methodologies, technologies, and strategies aimed at optimizing energy usage in IoT-based wireless sensor networks. Authored by experts from academia and industry, the book covers topics such as energy-aware routing protocols, edge computing, energy harvesting technologies, machine learning applications, and blockchain-based energy management frameworks. Each chapter provides cutting-edge insights and practical approaches to fostering energy efficiency while ensuring robust and scalable IoT solutions. This book serves as a valuable resource for researchers, professionals, and policymakers, offering actionable knowledge to navigate the evolving landscape of IoT and wireless sensor network technologies. Readers will find the volume: Explores the intersection of Internet of Things, wireless sensor networks, and energy efficiency across fundamental concepts, protocols, applications, and advanced solutions; Features chapters by leading researchers and industry professionals, providing authoritative perspectives; Offers actionable insights for implementing sustainable and energy-efficient IoT solutions in diverse fields like healthcare, agriculture, and smart cities; Highlights emerging trends, including AI, machine learning, and blockchain integration in wireless sensor networks. Audience Researchers, academics, engineers, system architects, technology developers, and policymakers specializing in IoT, wireless communications, and energy-efficient systems.

Tackle the critical challenge of sustainability in modern technology with this essential book, which provides a comprehensive, expert-led exploration of energy-aware methodologies and machine learning strategies for optimizing IoT-based wireless sensor networks. With the rapid expansion of IoT applications in diverse domains such as smart cities, agriculture, healthcare, and industry, energy constraints pose significant challenges to maintaining sustainable operations. This book addresses these challenges by presenting a comprehensive exploration of methodologies, technologies, and strategies aimed at optimizing energy usage in IoT-based wireless sensor networks. Authored by experts from academia and industry, the book covers topics such as energy-aware routing protocols, edge computing, energy harvesting technologies, machine learning applications, and blockchain-based energy management frameworks. Each chapter provides cutting-edge insights and practical approaches to fostering energy efficiency while ensuring robust and scalable IoT solutions. This book serves as a valuable resource for researchers, professionals, and policymakers, offering actionable knowledge to navigate the evolving landscape of IoT and wireless sensor network technologies. Readers will find the volume: Explores the intersection of Internet of Things, wireless sensor networks, and energy efficiency across fundamental concepts, protocols, applications, and advanced solutions; Features chapters by leading researchers and industry professionals, providing authoritative perspectives; Offers actionable insights for implementing sustainable and energy-efficient IoT solutions in diverse fields like healthcare, agriculture, and smart cities; Highlights emerging trends, including AI, machine learning, and blockchain integration in wireless sensor networks. Audience Researchers, academics, engineers, system architects, technology developers, and policymakers specializing in IoT, wireless communications, and energy-efficient systems.

This book explores the intersection of artificial intelligence and cybersecurity, focusing on advanced computational methods for identifying and responding to cyber threats in information systems. It examines how machine learning, deep learning, and other AI technologies can be  deployed to detect, prevent, and mitigate various forms of cyber-attacks, ranging from malware and network intrusions to sophisticated advanced persistent threats (APTs)